Generalizability TheoryGeneralizability theory offers an extensive conceptual framework and a powerful set of statistical procedures for characterizing and quantifying the fallibility of measurements. It liberalizes classical test theory, in part through the application of analysis of variance procedures that focus on variance components. As such, generalizability theory is perhaps the most broadly defined measurement model currently in existence. It is applicable to virtually any scientific field that attends to measurements and their errors, and it enables a multifacteted perspective on measurement error and its components. This book provides the most comprehensive and up-to-date treatment of generalizability theory. In addition, it provides a synthesis of those parts of the statistical literature that are directly applicable to generalizability theory. The principal intended audience is measurement practitioners and graduate students in the behavioral and social sciences, although a few examples and references are provided from other fields. Readers will benefit from some familiarity with classical test theory and analysis of variance, but the treatment of most topics does not presume specific background. Robert L. Brennan is E.F. Lindquist Professor of Educational Measurement at the University of Iowa. He is an acknowledged expert in generalizability theory, has authored numerous publications on the theory, and has taught many courses and workshops on generalizability. The author has been Vice-President of the American Educational Research Association and President of the National Council on Measurement in Education (NCME). He has received NCME Awards for Outstanding Technical Contributions to Educational Measurement and Career Contributions to Educational Measurement. |
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Inhoudsopgave
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Overige edities - Alles bekijken
Veelvoorkomende woorden en zinsdelen
admissible observations analogous analysis apply associated assumed assumptions average balanced designs Brennan chapter coefficient composite computations confidence intervals consider correlation covariance components D study Data Set defined diagram discussed districts Ep² equal Equation error variance estimated variance components example expected value expressed facet Figure fixed follows forms G study generalizability theory given gives illustrated indices involves issues levels linear matrices mean scores mean squares measurement multivariate nested normal notational Note ô² observed score obtained occasion particular persons population prediction procedure provides random effects raters reason regression relative error reliability replacing reported respectively sample sizes simply single specific standard statistical study design study estimated Suppose synthetic data Table tasks unbalanced universe of admissible universe score universe score variance usually variability weights
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